26 zone thin high CO2 cost

folder = "26z-thin-debug-highCCScost"
import os
from typing import Dict, List

import pandas as pd
import altair as alt
from pathlib import Path
from fig_functions import fix_tx_line_names, load_genx_operations_data, load_data

alt.renderers.enable("html")

cwd = Path.cwd()
data_path = cwd.parent / folder



fig_num = 0

cap = load_data(data_path, "resource_capacity.csv")
cap = cap.query("unit=='MW' and not tech_type.isna()")
cap["additions"] = cap["end_value"] - cap["start_value"]

gen = load_data(data_path, "generation.csv")

tx = load_data(data_path, "transmission.csv")


tx["start_region"] = tx["line_name"].str.split("_to_").str[0]
tx["dest_region"] = tx["line_name"].str.split("_to_").str[1]

first_year = tx["planning_year"].min()
starting_tx = tx.loc[tx["planning_year"] == first_year, :]
starting_tx = starting_tx.rename(columns={"start_value": "value"})
starting_tx["planning_year"] = 2023

tx_exp = load_data(data_path, "transmission_expansion.csv")
tx_exp["start_region"] = tx_exp["line_name"].str.split("_to_").str[0]
tx_exp["dest_region"] = tx_exp["line_name"].str.split("_to_").str[1]

emiss = load_data(data_path, "emissions.csv")

emiss.loc[emiss["unit"] == "kg", "value"] /= 1000


dispatch = load_data(data_path, "dispatch.csv")

dispatch = dispatch.groupby(
    [
        "planning_year",
        "model",
        "agg_zone",
        "zone",
        "tech_type",
        "resource_name",
        "hour",
    ],
    as_index=False,
)["value"].sum()
group_cols = ["planning_year", "model", "agg_zone", "zone", "tech_type"]
hours = dispatch["hour"].unique()
index_cols = ["resource_name"]
df_list = []
for _, _df in dispatch.groupby(group_cols):
    multi_index = pd.MultiIndex.from_product(
        [_df[col].unique() for col in index_cols] + [hours],
        names=index_cols + ["hour"],
    )
    _df = _df.set_index(index_cols + ["hour"])
    _df = _df.reindex(index=multi_index, fill_value=0)
    _df = _df.reset_index()
    for val, col in zip(_, group_cols):
        _df[col] = val
    df_list.append(_df)

dispatch = pd.concat(df_list, ignore_index=True)

Capacity and Generation

Row

Column

cap_data = cap.groupby(["tech_type", "model", "planning_year"], as_index=False)[
    "end_value"
].sum()

chart = (
    alt.Chart(cap_data)
    .mark_bar()
    .encode(
        x="model",
        y=alt.Y("sum(end_value)").title("Capacity (MW)"),
        color=alt.Color("tech_type").scale(scheme="tableau20"),
        # column="zone",
        row="planning_year:O",
        tooltip=alt.Tooltip("end_value", title="Capacity (MW)", format=",.0f")
    )
    .properties(width=350, height=250)
)
chart

data = cap.groupby(["agg_zone", "tech_type", "model", "planning_year"], as_index=False)[
    "end_value"
].sum()
chart = (
    alt.Chart(data)
    .mark_bar()
    .encode(
        x="model",
        y=alt.Y("end_value").title("Capacity (MW)"),
        color=alt.Color("tech_type").scale(scheme="tableau20"),
        column="agg_zone",
        row="planning_year:O",
        tooltip=alt.Tooltip("end_value", title="Capacity (MW)", format=",.0f")
    )
    .properties(width=150, height=250)
)
chart

Column

data = gen.groupby(["tech_type", "model", "planning_year"], as_index=False)[
    "value"
].sum()
# ojs_define(gendata = data)
chart = (
    alt.Chart(data)
    .mark_bar()
    .encode(
        x="model",
        y=alt.Y("value").title("Generation (MWh)"),
        color=alt.Color("tech_type").scale(scheme="tableau20"),
        # column="zone",
        row="planning_year:O",
        tooltip=alt.Tooltip("value", title="Generation (MWh)", format=",.0f")
    )
    .properties(width=350, height=250)
)
chart

data = gen.groupby(["agg_zone", "tech_type", "model", "planning_year"], as_index=False)[
    "value"
].sum()
chart = (
    alt.Chart(data)
    .mark_bar()
    .encode(
        x="model",
        y=alt.Y("value").title("Generation (MWh)"),
        color=alt.Color("tech_type").scale(scheme="tableau20"),
        column="agg_zone",
        row="planning_year:O",
        tooltip=alt.Tooltip("value", title="Generation (MWh)", format=",.0f")
    )
    .properties(width=150, height=250)
)
chart

Transmission Expansion

Row

chart = (
    alt.Chart(pd.concat([starting_tx, tx_exp]))
    .mark_bar()
    .encode(
        # xOffset="model:N",
        x="model",
        y=alt.Y("sum(value)").title("Total transmission expansion (MW)"),
        color="model:N",
        opacity=alt.Opacity("planning_year:O", sort="descending"),
        facet=alt.Facet("line_name", columns=10),
        order=alt.Order(
            # Sort the segments of the bars by this field
            "planning_year",
            sort="ascending",
        ),
        tooltip=alt.Tooltip("sum(value)", format=",.0f")
    )
)
chart

Emissions

alt.data_transformers.disable_max_rows()
base = (
    alt.Chart(emiss)
    .mark_bar()
    .encode(
        x="model",
        y=alt.Y("value").title("CO2 emissions (tonnes)"),
        color=alt.Color("zone").scale(scheme="tableau20"),
        # column="agg_zone",
        row="planning_year:O",
        tooltip=alt.Tooltip("value", format=",.0f")
    )
    .properties(width=350, height=250)
    .resolve_scale(y="independent")
)
# text = (
#     alt.Chart(emiss)
#     .mark_text(dy=3)
#     .encode(
#         x="model",
#         y=alt.Y("sum(value)"),
#         # color=alt.Color("zone").scale(scheme="tableau20"),
#         # column="agg_zone",
#         row="planning_year:O",
#         text="sum(value)"
#     )
#     .properties(width=350, height=250)
#     .resolve_scale(y="independent")
# )
chart

Dispatch

Row

data = dispatch.query("planning_year==2030").groupby(["model", "tech_type", "agg_zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", row="tech_type", column="agg_zone")
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

data = dispatch.query("planning_year==2040").groupby(["model", "tech_type", "agg_zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", row="tech_type", column="agg_zone")
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

data = dispatch.query("planning_year==2050").groupby(["model", "tech_type", "agg_zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", row="tech_type", column="agg_zone")
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

wind_dispatch = dispatch.query("resource_name.str.contains('landbasedwind')")
wind_dispatch["cluster"] = wind_dispatch["resource_name"].str.split("_").str[-1]
data = wind_dispatch.query("planning_year==2030").groupby(["model", "tech_type", "cluster", "zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", strokeDash="cluster",
            facet=alt.Facet("zone", columns=5))
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

/tmp/ipykernel_2236/3568967919.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  wind_dispatch["cluster"] = wind_dispatch["resource_name"].str.split("_").str[-1]

data = wind_dispatch.query("planning_year==2040").groupby(["model", "tech_type", "cluster", "zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", strokeDash="cluster",
            facet=alt.Facet("zone", columns=5))
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

data = wind_dispatch.query("planning_year==2050").groupby(["model", "tech_type", "cluster", "zone", "hour"], as_index=False)[
    "value"
].sum()
alt.data_transformers.disable_max_rows()
chart = (
    alt.Chart(data)
    .mark_line()
    .encode(x="hour", y="value", color="model", strokeDash="cluster",
            facet=alt.Facet("zone", columns=5))
    .properties(width=250, height=150)
).resolve_scale(y="independent")
chart

Operational Results

Row

op_costs = load_genx_operations_data(data_path, "costs.csv")
fig_num += 1
chart = (
    alt.Chart(
        op_costs[["Costs", "Total", "model"]].query("Total>0 and Costs != 'cTotal'")
    )
    .mark_bar()
    .encode(
        # xOffset="model:N",
        x="model:N",
        y=alt.Y("Total").title("Costs"),
        color="Costs:N",
        tooltip=alt.Tooltip("Total", format=",.0f")
    )
)
chart

op_nse = load_genx_operations_data(data_path, "nse.csv")
fig_num += 1
chart = (
    alt.Chart(op_nse[["Segment", "Total", "model"]].query("Segment == 'AnnualSum'"))
    .mark_bar()
    .encode(
        # xOffset="model:N",
        x="model:N",
        y=alt.Y("Total").title("Annual non-served MWh"),
        color="model:N",
        tooltip=alt.Tooltip("Total", format=",.0f")
    )
)
chart